Automatic recognition of partially occluded objects that are sensed by imaging sensors is a challenging problem in image understanding (IU), automatic target recognition (ATR), and computer vision fields. In this paper I address this problem by using a genetic algorithm (GA) as part of a model-based recognition scheme. The partially occluded object segments are rotated, translated, and scaled. Then each transform parameter is encoded into a binary string and used in a genetic algorithm. The suggested transformation is then applied to the sensed segment and the resulting object is matched against a library of stored targets. The fitness criterion is a distance function that measures the similarity between the segmented object and the stored target models. The GA by performing the process of mutation, reproduction, and crossover suggests optimum transform parameter sets. The empirical results of the application of the approach on a set of real ladar data of military targets shows that correct recognition for up to 50% target occlusion is possible.